MMR-and-RMR-workflow-Clutch85
Elliott Schmidt
2024-06-19
Load libraries (packages)
System1 - Dell
Importing data from firesting for resting
preexperiment_date <- "02 May 2023 12 43PM/All"
postexperiment_date <- "02 May 2023 05 47PM/All"
##--- last fish run in trial ---##
experiment_date <- "02 May 2023 02 36PM/Oxygen"
experiment_date2 <- "02 May 2023 02 36PM/All"
firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) System2 - Asus
Importing data from firesting for resting
preexperiment_date_asus <- "02 May 2023 12 18PM/All"
postexperiment_date_asus <- "02 May 2023 05 18PM/All"
##--- last fish run in trial ---##
experiment_date_asus <- "02 May 2023 01 41PM/Oxygen"
experiment_date2_asus <- "02 May 2023 01 41PM/All"
firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) Chamber volumes
chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860
chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791
Date_tested="2023-05-02"
Clutch = "84"
Male = "CARL233"
Female = "CARL215"
Population = "Arlington reef"
Tank =366
salinity =36
Date_analysed = Sys.Date() Replicates
1
Enter specimen data
Replicate = 1
mass = 0.0004984
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes="check max; switched with chamber one for background pre-trial"
##--- time of trail ---##
experiment_mmr_date <- "02 May 2023 02 36PM/Oxygen"
experiment_mmr_date2 <- "02 May 2023 02 36PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch1")) # custom
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch1")) # custom
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch1")) # custom
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.001044329post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0006801493Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 12.14
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 9 10 12 14 15 18 19 20 22 24 26 27 30 31
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 7.71
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 13 1 360.3350 -0.01960647 0.996 NA 5798 6032 13414.56
## 2: 16 1 385.5758 -0.01916027 0.997 NA 7282 7516 15033.68
## 3: 17 1 385.4858 -0.01850129 0.997 NA 7776 8010 15573.61
## 4: 19 1 433.8055 -0.02019276 0.998 NA 8765 8999 16654.35
## 5: 20 1 426.0199 -0.01909925 0.996 NA 9258 9491 17193.45
## 6: 21 1 417.4530 -0.01804155 0.997 NA 9752 9985 17734.33
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 13670.01 97.166 92.163 -0.01960647 -0.0002396270 -0.01936684 -0.01936684
## 2: 15289.56 97.272 92.588 -0.01916027 -0.0005191853 -0.01864108 -0.01864108
## 3: 15828.80 97.162 92.505 -0.01850129 -0.0006123381 -0.01788895 -0.01788895
## 4: 16910.01 97.300 92.303 -0.02019276 -0.0007989552 -0.01939380 -0.01939380
## 5: 17448.76 97.358 92.613 -0.01909925 -0.0008919940 -0.01820726 -0.01820726
## 6: 17989.68 97.449 92.781 -0.01804155 -0.0009853738 -0.01705617 -0.01705617
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.2095723
## 2: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.2017188
## 3: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.1935797
## 4: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.2098641
## 5: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.1970242
## 6: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.1845681
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -420.4902 NA mgO2/hr/kg -420.4902
## 2: -404.7327 NA mgO2/hr/kg -404.7327
## 3: -388.4023 NA mgO2/hr/kg -388.4023
## 4: -421.0756 NA mgO2/hr/kg -421.0756
## 5: -395.3134 NA mgO2/hr/kg -395.3134
## 6: -370.3213 NA mgO2/hr/kg -370.3213
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 1 | CARL233 | CARL215 | Arlington reef | 366 | 0.0004984 | ch4 | Dell | 0.0486 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 406.0028 | 0.2023518 | 0.9968 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 12.14
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 9 10 12 14 15 18 19 20 22 24 26 27 30 31
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 1.30
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 322.6357 -0.03241179 0.9934411 NA 218 273 7129.61
## 2: NA 2 320.7344 -0.03214626 0.9931427 NA 217 272 7128.52
## 3: NA 3 318.6166 -0.03185032 0.9932404 NA 216 271 7127.22
## 4: NA 4 316.0745 -0.03149514 0.9929944 NA 215 270 7126.13
## 5: NA 5 314.7891 -0.03131533 0.9933129 NA 214 269 7124.95
## ---
## 214: NA 214 211.9226 -0.01669303 0.9619293 NA 31 86 6923.18
## 215: NA 215 211.7305 -0.01666666 0.9612015 NA 35 90 6927.52
## 216: NA 216 210.3891 -0.01647280 0.9638121 NA 32 87 6924.26
## 217: NA 217 210.3332 -0.01646536 0.9641415 NA 34 89 6926.43
## 218: NA 218 209.7581 -0.01638242 0.9647639 NA 33 88 6925.34
## endtime oxy endoxy rate
## 1: 7189.61 91.498 89.549 -0.03241179
## 2: 7188.52 91.540 89.561 -0.03214626
## 3: 7187.22 91.532 89.612 -0.03185032
## 4: 7186.13 91.645 89.666 -0.03149514
## 5: 7184.95 91.633 89.796 -0.03131533
## ---
## 214: 6983.18 96.487 95.391 -0.01669303
## 215: 6987.52 96.313 95.135 -0.01666666
## 216: 6984.26 96.466 95.340 -0.01647280
## 217: 6986.43 96.329 95.227 -0.01646536
## 218: 6985.34 96.412 95.281 -0.01638242
##
## Regressions : 218 | Results : 218 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 218 adjusted rate(s):
## Rate : -0.03241179
## Adjustment : 0.001044329
## Adjusted Rate : -0.03345612
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 218 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 217 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 322.6357 -0.03241179 0.9934411 NA 218 273 7129.61
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7189.61 91.498 89.549 -0.03241179 0.001044329 -0.03345612 -0.03345612
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0004984 NA 36 30 1.013253 -0.3620351
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -726.3946 NA mgO2/hr/kg -726.3946
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 1 | CARL233 | CARL215 | Arlington reef | 366 | 0.0004984 | ch4 | Dell | 0.0486 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 406.0028 | 0.2023518 | 0.9968 | 726.3946 | 0.3620351 | 0.9934411 | 320.3918 | 0.1596833 | check max; switched with chamber one for background pre-trial |
Exporting data
## Rows: 168 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.2
Enter specimen data
Replicate = 2
mass = 0.0005475
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "02 May 2023 02 08PM/Oxygen"
experiment_mmr_date2 <- "02 May 2023 02 08PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0005203475post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.002069291Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 12.14
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 9 10 12 14 15 18 19 20 22 24 26 27 30 31
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 7.71
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 6 1 306.9503 -0.02157972 0.987 NA 2346 2578 9634.16
## 2: 9 1 330.3417 -0.02059347 0.986 NA 3822 4056 11254.36
## 3: 11 1 342.7452 -0.01976142 0.987 NA 4810 5042 12333.54
## 4: 12 1 406.8781 -0.02393456 0.971 NA 5304 5538 12874.25
## 5: 17 1 463.3515 -0.02341578 0.984 NA 7776 8010 15573.61
## 6: 18 1 455.2100 -0.02217102 0.985 NA 8271 8504 16114.27
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9889.30 98.611 93.641 -0.02157972 -0.001087378 -0.02049234 -0.02049234
## 2: 11510.11 98.482 93.110 -0.02059347 -0.001338662 -0.01925480 -0.01925480
## 3: 12588.24 98.652 94.165 -0.01976142 -0.001505923 -0.01825549 -0.01825549
## 4: 13129.79 98.658 91.869 -0.02393456 -0.001589834 -0.02234473 -0.02234473
## 5: 15828.80 98.447 92.991 -0.02341578 -0.002008383 -0.02140740 -0.02140740
## 6: 16368.90 97.887 92.134 -0.02217102 -0.002092177 -0.02007885 -0.02007885
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2270900
## 2: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2133760
## 3: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2023019
## 4: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2476176
## 5: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2372305
## 6: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.2225078
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -414.7763 NA mgO2/hr/kg -414.7763
## 2: -389.7279 NA mgO2/hr/kg -389.7279
## 3: -369.5013 NA mgO2/hr/kg -369.5013
## 4: -452.2696 NA mgO2/hr/kg -452.2696
## 5: -433.2976 NA mgO2/hr/kg -433.2976
## 6: -406.4069 NA mgO2/hr/kg -406.4069
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 2 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005475 | ch3 | Dell | 0.04977 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 419.2957 | 0.2295644 | 0.9826 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 12.14
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 20 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.35
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 386.0308 -0.05487001 0.9983771 NA 130 183 5342.16
## 2: NA 2 385.9754 -0.05485953 0.9983745 NA 131 184 5343.48
## 3: NA 3 385.8790 -0.05484136 0.9983630 NA 132 185 5344.59
## 4: NA 4 385.3191 -0.05473763 0.9983230 NA 129 182 5341.07
## 5: NA 5 385.2717 -0.05472901 0.9983151 NA 128 181 5339.98
## ---
## 205: NA 205 303.3227 -0.03928444 0.9975562 NA 8 61 5201.90
## 206: NA 206 303.1908 -0.03925882 0.9976182 NA 7 60 5200.57
## 207: NA 207 303.0076 -0.03922480 0.9974314 NA 3 56 5195.97
## 208: NA 208 302.4479 -0.03911798 0.9972496 NA 2 55 5194.87
## 209: NA 209 302.3223 -0.03909393 0.9972510 NA 1 54 5193.54
## endtime oxy endoxy rate
## 1: 5402.16 92.900 89.596 -0.05487001
## 2: 5403.48 92.860 89.568 -0.05485953
## 3: 5404.59 92.791 89.522 -0.05484136
## 4: 5401.07 92.976 89.704 -0.05473763
## 5: 5399.98 93.001 89.756 -0.05472901
## ---
## 205: 5261.90 99.049 96.548 -0.03928444
## 206: 5260.57 99.066 96.699 -0.03925882
## 207: 5255.97 99.149 96.853 -0.03922480
## 208: 5254.87 99.255 96.881 -0.03911798
## 209: 5253.54 99.297 96.924 -0.03909393
##
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 209 adjusted rate(s):
## Rate : -0.05487001
## Adjustment : -0.0005203475
## Adjusted Rate : -0.05434966
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 209 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 208 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 386.0308 -0.05487001 0.9983771 NA 130 183 5342.16
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 5402.16 92.9 89.596 -0.05487001 -0.0005203475 -0.05434966 -0.05434966
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0005475 NA 36 30 1.013253 -0.6022868
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1100.067 NA mgO2/hr/kg -1100.067
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 2 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005475 | ch3 | Dell | 0.04977 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 419.2957 | 0.2295644 | 0.9826 | 1100.067 | 0.6022868 | 0.9983771 | 680.7715 | 0.3727224 |
Exporting data
## Rows: 169 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3
Enter specimen data
Replicate = 3
mass = 0.0004428
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "02 May 2023 02 18PM/Oxygen"
experiment_mmr_date2 <- "02 May 2023 02 18PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -6.25976e-05post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0009106014Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 12.14
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 9 10 12 14 15 18 19 20 22 24 26 27 30 31
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 7.71
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 4 rate(s) removed, 17 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 11 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 1 1 229.5598 -0.01880644 0.994 NA 42 273 6934.43
## 2: 5 1 252.4230 -0.01698588 0.986 NA 1860 2089 9094.70
## 3: 6 1 253.5667 -0.01611046 0.960 NA 2346 2578 9634.16
## 4: 8 1 302.9493 -0.01904338 0.981 NA 3328 3561 10714.32
## 5: 13 1 366.3772 -0.01997054 0.965 NA 5798 6032 13414.56
## 6: 18 1 437.5838 -0.02100011 0.958 NA 8271 8504 16114.27
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7189.61 98.857 94.306 -0.01880644 -0.0001438426 -0.01866260 -0.01866260
## 2: 9349.32 98.104 93.642 -0.01698588 -0.0003272123 -0.01665866 -0.01665866
## 3: 9889.30 98.713 93.885 -0.01611046 -0.0003730312 -0.01573743 -0.01573743
## 4: 10969.27 98.980 93.782 -0.01904338 -0.0004647219 -0.01857866 -0.01857866
## 5: 13670.01 98.391 93.560 -0.01997054 -0.0006939767 -0.01927657 -0.01927657
## 6: 16368.90 98.953 93.241 -0.02100011 -0.0009231304 -0.02007698 -0.02007698
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.1908567
## 2: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.1703631
## 3: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.1609419
## 4: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.1899983
## 5: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.1971355
## 6: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.2053212
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -431.0223 NA mgO2/hr/kg -431.0223
## 2: -384.7404 NA mgO2/hr/kg -384.7404
## 3: -363.4640 NA mgO2/hr/kg -363.4640
## 4: -429.0837 NA mgO2/hr/kg -429.0837
## 5: -445.2022 NA mgO2/hr/kg -445.2022
## 6: -463.6883 NA mgO2/hr/kg -463.6883
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 3 | CARL233 | CARL215 | Arlington reef | 366 | 0.0004428 | ch2 | Dell | 0.04593 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 430.7474 | 0.1907349 | 0.9768 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 12.14
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row + 100, # Custom
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 5 7 8 9 11 13 14 15 17 18 20 21 22 23 25 26 27
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.57
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 347.5038 -0.04305287 0.9975462 NA 34 88 5938.15
## 2: NA 2 347.5017 -0.04305220 0.9975342 NA 36 90 5940.32
## 3: NA 3 347.4572 -0.04304475 0.9975323 NA 37 91 5941.41
## 4: NA 4 347.4022 -0.04303918 0.9965582 NA 1 55 5901.07
## 5: NA 5 347.3810 -0.04303209 0.9975200 NA 35 89 5939.24
## ---
## 111: NA 111 282.5724 -0.03226561 0.9889684 NA 111 165 6024.43
## 112: NA 112 281.2450 -0.03204666 0.9893928 NA 112 166 6025.51
## 113: NA 113 279.5386 -0.03176511 0.9897674 NA 113 167 6026.60
## 114: NA 114 278.3896 -0.03157557 0.9899280 NA 114 168 6027.69
## 115: NA 115 277.5080 -0.03143029 0.9901595 NA 115 169 6028.78
## endtime oxy endoxy rate
## 1: 5998.15 91.837 89.279 -0.04305287
## 2: 6000.32 91.758 89.186 -0.04305220
## 3: 6001.41 91.735 89.131 -0.04304475
## 4: 5961.07 93.623 90.834 -0.04303918
## 5: 5999.24 91.799 89.259 -0.04303209
## ---
## 111: 6084.43 88.286 86.262 -0.03226561
## 112: 6085.51 88.267 86.239 -0.03204666
## 113: 6086.60 88.229 86.238 -0.03176511
## 114: 6087.69 88.146 86.193 -0.03157557
## 115: 6088.78 88.104 86.139 -0.03143029
##
## Regressions : 115 | Results : 115 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 115 adjusted rate(s):
## Rate : -0.04305287
## Adjustment : -6.25976e-05
## Adjusted Rate : -0.04299028
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 115 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 114 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 347.5038 -0.04305287 0.9975462 NA 34 88 5938.15
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 5998.15 91.837 89.279 -0.04305287 -6.25976e-05 -0.04299028 -0.04299028
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.0004428 NA 36 30 1.013253 -0.4396484
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -992.8826 NA mgO2/hr/kg -992.8826
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 3 | CARL233 | CARL215 | Arlington reef | 366 | 0.0004428 | ch2 | Dell | 0.04593 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 430.7474 | 0.1907349 | 0.9768 | 992.8826 | 0.4396484 | 0.9975462 | 562.1353 | 0.2489135 |
Exporting data
## Rows: 170 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4
Enter specimen data
Replicate = 4
mass = 0.0007714
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "02 May 2023 02 27PM/Oxygen"
experiment_mmr_date2 <- "02 May 2023 02 27PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch4")) # custom
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch4")) # custom
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time","ch4")) # custom
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00193549post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001395475Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 12.14
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 9 10 12 14 15 18 19 20 22 24 26 27 30 31
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 7.71
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 400.6557 -0.02711886 0.987 NA 3822 4056 11254.36
## 2: 10 1 477.8724 -0.03238822 0.995 NA 4316 4550 11794.13
## 3: 11 1 385.9311 -0.02353975 0.952 NA 4810 5042 12333.54
## 4: 17 1 601.7448 -0.03249479 0.997 NA 7776 8010 15573.61
## 5: 18 1 619.1657 -0.03236419 0.987 NA 8271 8504 16114.27
## 6: 19 1 646.2275 -0.03295225 0.991 NA 8765 8999 16654.35
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11510.11 95.768 88.691 -0.02711886 -0.001650198 -0.02546866 -0.02546866
## 2: 12049.54 96.041 87.417 -0.03238822 -0.001621026 -0.03076719 -0.03076719
## 3: 12588.24 95.847 88.683 -0.02353975 -0.001591885 -0.02194786 -0.02194786
## 4: 15828.80 95.687 87.548 -0.03249479 -0.001416710 -0.03107808 -0.03107808
## 5: 16368.90 96.970 89.416 -0.03236419 -0.001387497 -0.03097670 -0.03097670
## 6: 16910.01 97.173 88.629 -0.03295225 -0.001358271 -0.03159398 -0.03159398
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.2636926
## 2: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.3185515
## 3: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.2272396
## 4: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.3217703
## 5: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.3207206
## 6: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.3271117
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -341.8364 NA mgO2/hr/kg -341.8364
## 2: -412.9524 NA mgO2/hr/kg -412.9524
## 3: -294.5808 NA mgO2/hr/kg -294.5808
## 4: -417.1252 NA mgO2/hr/kg -417.1252
## 5: -415.7644 NA mgO2/hr/kg -415.7644
## 6: -424.0495 NA mgO2/hr/kg -424.0495
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 4 | CARL233 | CARL215 | Arlington reef | 366 | 0.0007714 | ch1 | Dell | 0.0465 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 402.3456 | 0.3103694 | 0.9914 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 12.14
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 11 12 13 14 18 19 20 22 23 25 26 27
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.32
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 629.6833 -0.08343332 0.9941930 NA 122 176 6480.68
## 2: NA 2 629.5236 -0.08340739 0.9941392 NA 123 177 6481.77
## 3: NA 3 628.1873 -0.08333245 0.9967303 NA 34 88 6383.51
## 4: NA 4 627.9846 -0.08330151 0.9967621 NA 35 89 6384.65
## 5: NA 5 627.9528 -0.08329532 0.9967613 NA 33 87 6382.43
## ---
## 214: NA 214 397.6914 -0.04801510 0.9818869 NA 209 263 6576.57
## 215: NA 215 397.2221 -0.04794572 0.9817284 NA 213 267 6580.92
## 216: NA 216 395.9659 -0.04775521 0.9820581 NA 212 266 6579.84
## 217: NA 217 395.9578 -0.04775304 0.9820919 NA 210 264 6577.66
## 218: NA 218 395.5278 -0.04768841 0.9821793 NA 211 265 6578.75
## endtime oxy endoxy rate
## 1: 6540.68 88.729 84.160 -0.08343332
## 2: 6541.77 88.701 84.145 -0.08340739
## 3: 6443.51 96.342 91.130 -0.08333245
## 4: 6444.65 96.286 91.029 -0.08330151
## 5: 6442.43 96.391 91.235 -0.08329532
## ---
## 214: 6636.57 82.014 79.113 -0.04801510
## 215: 6640.92 81.702 78.681 -0.04794572
## 216: 6639.84 81.746 78.770 -0.04775521
## 217: 6637.66 81.925 79.003 -0.04775304
## 218: 6638.75 81.836 78.874 -0.04768841
##
## Regressions : 218 | Results : 218 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 218 adjusted rate(s):
## Rate : -0.08343332
## Adjustment : -0.00193549
## Adjusted Rate : -0.08149783
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 218 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 217 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 629.6833 -0.08343332 0.994193 NA 122 176 6480.68
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6540.68 88.729 84.16 -0.08343332 -0.00193549 -0.08149783 -0.08149783
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0007714 NA 36 30 1.013253 -0.8437968
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1093.851 NA mgO2/hr/kg -1093.851
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 4 | CARL233 | CARL215 | Arlington reef | 366 | 0.0007714 | ch1 | Dell | 0.0465 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 402.3456 | 0.3103694 | 0.9914 | 1093.851 | 0.8437968 | 0.994193 | 691.5056 | 0.5334274 |
Exporting data
## Rows: 171 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5
Enter specimen data
Replicate = 5
mass = 0.0005537
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "02 May 2023 12 55PM/Oxygen"
experiment_mmr_date2_asus <- "02 May 2023 12 55PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 7.463927e-05post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -1.812834e-05Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 5 1 305.8413 -0.02837206 0.998 NA 1614 1801 7264.68
## 2: 8 1 352.8987 -0.02854359 0.999 NA 2801 2988 8885.95
## 3: 15 1 463.8793 -0.02880443 0.999 NA 5565 5751 12665.63
## 4: 19 1 502.3958 -0.02718265 1.000 NA 7143 7329 14825.62
## 5: 20 1 533.9857 -0.02826991 0.999 NA 7537 7723 15364.90
## 6: 21 1 531.3672 -0.02715134 0.999 NA 7932 8118 15905.49
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7519.99 99.553 92.345 -0.02837206 3.258887e-05 -0.02840465 -0.02840465
## 2: 9141.26 99.085 91.877 -0.02854359 1.405969e-05 -0.02855765 -0.02855765
## 3: 12920.40 98.931 91.678 -0.02880443 -2.913445e-05 -0.02877529 -0.02877529
## 4: 15080.29 99.213 92.422 -0.02718265 -5.381998e-05 -0.02712883 -0.02712883
## 5: 15619.33 99.424 92.406 -0.02826991 -5.998194e-05 -0.02820992 -0.02820992
## 6: 16160.09 99.336 92.538 -0.02715134 -6.616121e-05 -0.02708518 -0.02708518
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.3030082
## 2: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.3046404
## 3: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.3069621
## 4: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.2893984
## 5: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.3009310
## 6: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.2889327
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -547.2426 NA mgO2/hr/kg -547.2426
## 2: -550.1903 NA mgO2/hr/kg -550.1903
## 3: -554.3834 NA mgO2/hr/kg -554.3834
## 4: -522.6628 NA mgO2/hr/kg -522.6628
## 5: -543.4911 NA mgO2/hr/kg -543.4911
## 6: -521.8217 NA mgO2/hr/kg -521.8217
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 5 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005537 | ch4 | Asus | 0.04791 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 543.594 | 0.300988 | 0.999 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.50
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.start.row+100, # custom
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 5 6 7 8 9 10 13 14 15 18 19 20 21 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.40
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 206.7453 -0.04575109 0.9984406 NA 21 66 2340.12
## 2: NA 2 206.7000 -0.04573148 0.9984225 NA 22 67 2341.49
## 3: NA 3 206.6969 -0.04573109 0.9984280 NA 19 64 2337.41
## 4: NA 4 206.6935 -0.04572957 0.9984275 NA 20 65 2338.76
## 5: NA 5 206.5868 -0.04568526 0.9985812 NA 35 80 2359.25
## ---
## 52: NA 52 191.7105 -0.03948305 0.9918747 NA 53 98 2383.61
## 53: NA 53 191.9632 -0.03948281 0.9849570 NA 1 46 2312.90
## 54: NA 54 190.9792 -0.03917993 0.9915972 NA 54 99 2384.97
## 55: NA 55 190.2180 -0.03886473 0.9913784 NA 55 100 2386.32
## 56: NA 56 189.4487 -0.03854665 0.9913888 NA 56 101 2387.68
## endtime oxy endoxy rate
## 1: 2400.12 99.716 96.950 -0.04575109
## 2: 2401.49 99.655 96.907 -0.04573148
## 3: 2397.41 99.829 97.049 -0.04573109
## 4: 2398.76 99.794 97.005 -0.04572957
## 5: 2419.25 98.833 96.083 -0.04568526
## ---
## 52: 2443.61 97.760 95.286 -0.03948305
## 53: 2372.90 100.470 98.230 -0.03948281
## 54: 2444.97 97.644 95.262 -0.03917993
## 55: 2446.32 97.589 95.216 -0.03886473
## 56: 2447.68 97.545 95.156 -0.03854665
##
## Regressions : 56 | Results : 56 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 56 adjusted rate(s):
## Rate : -0.04575109
## Adjustment : 7.463927e-05
## Adjusted Rate : -0.04582573
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 56 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 55 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 206.7453 -0.04575109 0.9984406 NA 21 66 2340.12
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 2400.12 99.716 96.95 -0.04575109 7.463927e-05 -0.04582573 -0.04582573
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0005537 NA 36 30 1.013253 -0.4888486
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -882.8763 NA mgO2/hr/kg -882.8763
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 5 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005537 | ch4 | Asus | 0.04791 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 543.594 | 0.300988 | 0.999 | 882.8763 | 0.4888486 | 0.9984406 | 339.2822 | 0.1878606 | ||
| ### Expor | ting data |
## Rows: 172 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.6
Enter specimen data
Replicate = 6
mass = 0.0005741
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "02 May 2023 01 05PM/Oxygen"
experiment_mmr_date2_asus <- "02 May 2023 01 05PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 5.103437e-05post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0008807193Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 6 1 318.4729 -0.02842025 0.970 NA 2010 2189 7805.64
## 2: 7 1 351.3819 -0.03048616 0.981 NA 2405 2585 8345.69
## 3: 8 1 335.6881 -0.02688384 0.976 NA 2801 2980 8885.95
## 4: 14 1 457.0255 -0.02966693 0.971 NA 5170 5349 12125.71
## 5: 15 1 472.0949 -0.02965106 0.977 NA 5565 5744 12665.63
## 6: 18 1 511.7371 -0.02904136 0.981 NA 6748 6927 14285.13
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 8050.51 96.753 89.178 -0.02842025 -0.0004328158 -0.02798744 -0.02798744
## 2: 8591.15 96.758 89.389 -0.03048616 -0.0004948422 -0.02999132 -0.02999132
## 3: 9130.39 96.388 89.931 -0.02688384 -0.0005568003 -0.02632704 -0.02632704
## 4: 12370.24 96.939 89.517 -0.02966693 -0.0009286988 -0.02873823 -0.02873823
## 5: 12910.85 96.909 89.665 -0.02965106 -0.0009907161 -0.02866034 -0.02866034
## 6: 14530.25 96.992 89.664 -0.02904136 -0.0011766134 -0.02786475 -0.02786475
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.2836017
## 2: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.3039073
## 3: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.2667765
## 4: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.2912096
## 5: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.2904203
## 6: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.2823585
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -493.9935 NA mgO2/hr/kg -493.9935
## 2: -529.3631 NA mgO2/hr/kg -529.3631
## 3: -464.6865 NA mgO2/hr/kg -464.6865
## 4: -507.2454 NA mgO2/hr/kg -507.2454
## 5: -505.8706 NA mgO2/hr/kg -505.8706
## 6: -491.8280 NA mgO2/hr/kg -491.8280
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 6 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005741 | ch3 | Asus | 0.04551 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 505.6601 | 0.2902995 | 0.976 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.50
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.34 1.55
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 277.3038 -0.06218525 0.9778666 NA 1 46 2873.25
## 2: NA 2 276.7460 -0.06199192 0.9774644 NA 2 47 2874.63
## 3: NA 3 276.4102 -0.06187534 0.9772580 NA 3 48 2876.00
## 4: NA 4 276.3465 -0.06185554 0.9768351 NA 15 60 2892.28
## 5: NA 5 276.1571 -0.06178718 0.9770886 NA 4 49 2877.38
## ---
## 173: NA 173 190.6730 -0.03335566 0.9587948 NA 124 169 3040.57
## 174: NA 174 190.5630 -0.03330803 0.9589624 NA 120 165 3035.13
## 175: NA 175 190.1168 -0.03316616 0.9605307 NA 121 166 3036.47
## 176: NA 176 189.9010 -0.03310125 0.9610417 NA 123 168 3039.21
## 177: NA 177 189.7495 -0.03304921 0.9614758 NA 122 167 3037.84
## endtime oxy endoxy rate
## 1: 2933.25 98.682 95.056 -0.06218525
## 2: 2934.63 98.579 94.945 -0.06199192
## 3: 2936.00 98.493 94.831 -0.06187534
## 4: 2952.28 97.598 93.607 -0.06185554
## 5: 2937.38 98.394 94.754 -0.06178718
## ---
## 173: 3100.57 89.436 86.998 -0.03335566
## 174: 3095.13 89.827 87.285 -0.03330803
## 175: 3096.47 89.740 87.202 -0.03316616
## 176: 3099.21 89.514 87.121 -0.03310125
## 177: 3097.84 89.615 87.205 -0.03304921
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.06218525
## Adjustment : 5.103437e-05
## Adjusted Rate : -0.06223628
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 2 rate(s) removed, 175 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 174 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 277.3038 -0.06218525 0.9778666 NA 1 46 2873.25
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 2933.25 98.682 95.056 -0.06218525 5.103437e-05 -0.06223628 -0.06223628
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04551 0.0005741 NA 36 30 1.013253 -0.6306514
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1098.504 NA mgO2/hr/kg -1098.504
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 6 | CARL233 | CARL215 | Arlington reef | 366 | 0.0005741 | ch3 | Asus | 0.04551 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 505.6601 | 0.2902995 | 0.976 | 1098.504 | 0.6306514 | 0.9778666 | 592.8442 | 0.3403519 | ||
| ### Expor | ting data |
## Rows: 173 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.7
Enter specimen data
Replicate = 7
mass = 0.0002893
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "02 May 2023 01 41PM/Oxygen"
experiment_mmr_date2_asus <- "02 May 2023 01 41PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_2.txt"), # custom
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.0001571243post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0006501595Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 7 rate(s) removed, 14 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 8 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 5 1 190.9708 -0.012505591 0.961 NA 1614 1794 7264.68
## 2: 9 1 217.7247 -0.012542136 0.988 NA 3196 3375 9425.26
## 3: 13 1 224.7875 -0.010776320 0.994 NA 4775 4954 11585.33
## 4: 15 1 217.2176 -0.009259912 0.972 NA 5565 5744 12665.63
## 5: 17 1 263.3202 -0.011857523 0.960 NA 6353 6532 13744.62
## 6: 21 1 319.5758 -0.013784486 0.989 NA 7932 8111 15905.49
## endtime oxy endoxy rate adjustment rate.adjusted
## 1: 7510.46 100.320 97.062 -0.012505591 -0.0002083335 -0.012297257
## 2: 9669.69 99.691 96.301 -0.012542136 -0.0004231489 -0.012118987
## 3: 11830.24 100.050 97.308 -0.010776320 -0.0006380045 -0.010138316
## 4: 12910.85 99.829 97.700 -0.009259912 -0.0007454622 -0.008514449
## 5: 13989.85 100.140 97.290 -0.011857523 -0.0008527747 -0.011004748
## 6: 16150.49 100.060 96.879 -0.013784486 -0.0010676746 -0.012716812
## rate.input oxy.unit time.unit volume mass area S t P
## 1: -0.012297257 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## 2: -0.012118987 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## 3: -0.010138316 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## 4: -0.008514449 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## 5: -0.011004748 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## 6: -0.012716812 %Air sec 0.04573 0.0002893 NA 36 30 1.013253
## rate.abs rate.m.spec rate.a.spec output.unit rate.output
## 1: -0.12521268 -432.8126 NA mgO2/hr/kg -432.8126
## 2: -0.12339750 -426.5382 NA mgO2/hr/kg -426.5382
## 3: -0.10322998 -356.8268 NA mgO2/hr/kg -356.8268
## 4: -0.08669551 -299.6734 NA mgO2/hr/kg -299.6734
## 5: -0.11205214 -387.3216 NA mgO2/hr/kg -387.3216
## 6: -0.12948465 -447.5791 NA mgO2/hr/kg -447.5791
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 7 | CARL233 | CARL215 | Arlington reef | 366 | 0.0002893 | ch2 | Asus | 0.04573 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 410.2157 | 0.1186754 | 0.9784 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.34 1.52
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 243.0742 -0.025339552 0.9852456 NA 24 68 5632.54
## 2: NA 2 242.8481 -0.025299351 0.9850726 NA 25 69 5633.91
## 3: NA 3 242.0187 -0.025152623 0.9842842 NA 26 70 5635.27
## 4: NA 4 241.2405 -0.025016564 0.9841684 NA 23 67 5631.02
## 5: NA 5 240.5532 -0.024895551 0.9833552 NA 22 66 5629.65
## ---
## 172: NA 172 123.7642 -0.004614695 0.6365110 NA 172 216 5834.17
## 173: NA 173 121.6189 -0.004248600 0.5785892 NA 173 217 5835.52
## 174: NA 174 118.9886 -0.003800057 0.5124425 NA 174 218 5836.88
## 175: NA 175 115.9217 -0.003277453 0.4447687 NA 175 219 5838.26
## 176: NA 176 113.1276 -0.002801425 0.3727197 NA 176 220 5839.62
## endtime oxy endoxy rate
## 1: 5692.54 100.250 98.865 -0.025339552
## 2: 5693.91 100.290 98.838 -0.025299351
## 3: 5695.27 100.270 98.839 -0.025152623
## 4: 5691.02 100.290 98.897 -0.025016564
## 5: 5689.65 100.320 98.964 -0.024895551
## ---
## 172: 5894.17 96.879 96.676 -0.004614695
## 173: 5895.52 96.902 96.690 -0.004248600
## 174: 5896.88 96.929 96.696 -0.003800057
## 175: 5898.26 96.895 96.693 -0.003277453
## 176: 5899.62 96.875 96.696 -0.002801425
##
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 176 adjusted rate(s):
## Rate : -0.02533955
## Adjustment : 0.0001571243
## Adjusted Rate : -0.02549668
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 53 rate(s) removed, 123 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 122 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 243.0742 -0.02533955 0.9852456 NA 24 68 5632.54
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 5692.54 100.25 98.865 -0.02533955 0.0001571243 -0.02549668 -0.02549668
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0002893 NA 36 30 1.013253 -0.2596113
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -897.3775 NA mgO2/hr/kg -897.3775
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 7 | CARL233 | CARL215 | Arlington reef | 366 | 0.0002893 | ch2 | Asus | 0.04573 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 410.2157 | 0.1186754 | 0.9784 | 897.3775 | 0.2596113 | 0.9852456 | 487.1618 | 0.1409359 | ||
| ### Expor | ting data |
## Rows: 174 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.8
Enter specimen data
Replicate = 8
mass = 0.0007933
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "02 May 2023 01 32PM/Oxygen"
experiment_mmr_date2_asus <- "02 May 2023 01 32PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.0007104622post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0004440985Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 4.51
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 14 1 393.5527 -0.02438592 0.992 NA 5170 5349 12125.71
## 2: 16 1 443.6477 -0.02616238 0.996 NA 5959 6138 13205.62
## 3: 17 1 462.5961 -0.02651171 0.990 NA 6353 6532 13744.62
## 4: 18 1 439.2806 -0.02388496 0.996 NA 6748 6927 14285.13
## 5: 19 1 500.4990 -0.02712346 0.986 NA 7143 7322 14825.62
## 6: 21 1 489.4192 -0.02458563 0.955 NA 7932 8111 15905.49
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 12370.24 97.541 91.686 -0.02438592 -0.0005035512 -0.02388237 -0.02388237
## 2: 13450.86 97.899 91.653 -0.02616238 -0.0006572079 -0.02550517 -0.02550517
## 3: 13989.85 97.784 91.521 -0.02651171 -0.0007338745 -0.02577783 -0.02577783
## 4: 14530.25 97.860 91.765 -0.02388496 -0.0008107487 -0.02307421 -0.02307421
## 5: 15070.67 97.840 91.576 -0.02712346 -0.0008876229 -0.02623584 -0.02623584
## 6: 16150.49 97.859 91.985 -0.02458563 -0.0010412199 -0.02354441 -0.02354441
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2427488
## 2: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2592435
## 3: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2620149
## 4: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2345344
## 5: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2666703
## 6: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.2393137
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -305.9987 NA mgO2/hr/kg -305.9987
## 2: -326.7913 NA mgO2/hr/kg -326.7913
## 3: -330.2848 NA mgO2/hr/kg -330.2848
## 4: -295.6440 NA mgO2/hr/kg -295.6440
## 5: -336.1531 NA mgO2/hr/kg -336.1531
## 6: -301.6685 NA mgO2/hr/kg -301.6685
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 8 | CARL233 | CARL215 | Arlington reef | 366 | 0.0007933 | ch1 | Asus | 0.04565 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 320.1793 | 0.2539982 | 0.9838 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.50
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 24
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.72
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 453.5033 -0.07877285 0.9956382 NA 85 129 4599.14
## 2: NA 2 453.3506 -0.07874942 0.9952608 NA 73 117 4582.84
## 3: NA 3 453.3794 -0.07874572 0.9956235 NA 86 130 4600.49
## 4: NA 4 453.2727 -0.07872362 0.9956015 NA 84 128 4597.78
## 5: NA 5 453.1491 -0.07870601 0.9952702 NA 74 118 4584.19
## ---
## 173: NA 173 352.8904 -0.05666554 0.9922849 NA 5 49 4490.25
## 174: NA 174 349.9540 -0.05601559 0.9925545 NA 4 48 4488.91
## 175: NA 175 346.9737 -0.05535540 0.9933175 NA 3 47 4487.37
## 176: NA 176 344.1508 -0.05472960 0.9944020 NA 2 46 4486.01
## 177: NA 177 341.1938 -0.05407422 0.9952013 NA 1 45 4484.65
## endtime oxy endoxy rate
## 1: 4659.14 91.174 86.539 -0.07877285
## 2: 4642.84 92.476 87.704 -0.07874942
## 3: 4660.49 91.136 86.430 -0.07874572
## 4: 4657.78 91.238 86.676 -0.07872362
## 5: 4644.19 92.367 87.605 -0.07870601
## ---
## 173: 4550.25 98.342 94.883 -0.05666554
## 174: 4548.91 98.431 94.944 -0.05601559
## 175: 4547.37 98.519 95.036 -0.05535540
## 176: 4546.01 98.544 95.153 -0.05472960
## 177: 4544.65 98.623 95.350 -0.05407422
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.07877285
## Adjustment : 0.0007104622
## Adjusted Rate : -0.07948332
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 453.5033 -0.07877285 0.9956382 NA 85 129 4599.14
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4659.14 91.174 86.539 -0.07877285 0.0007104622 -0.07948332 -0.07948332
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0007933 NA 36 30 1.013253 -0.8078963
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1018.399 NA mgO2/hr/kg -1018.399
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 84 | 8 | CARL233 | CARL215 | Arlington reef | 366 | 0.0007933 | ch1 | Asus | 0.04565 | 2023-05-02 | 2024-06-19 | good/good | 36 | 30 | 320.1793 | 0.2539982 | 0.9838 | 1018.399 | 0.8078963 | 0.9956382 | 698.2201 | 0.553898 | ||
| ### Expor | ting data |
## Rows: 175 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.